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Analysis of World Cup Finals. Outline Project Understanding – World Cup History Data Understanding – How to collect the data Data Manipulation – Data.

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Presentation on theme: "Analysis of World Cup Finals. Outline Project Understanding – World Cup History Data Understanding – How to collect the data Data Manipulation – Data."— Presentation transcript:

1 Analysis of World Cup Finals

2 Outline Project Understanding – World Cup History Data Understanding – How to collect the data Data Manipulation – Data Cleaning – Feature Selection – Missing Values Handling – Discretization and Normalization Data Visualization Modelling – Classification of matches – Regression of matches’ scores Association Rule Learning Conclusion

3 Project Understanding Domain is football. Peak of Football Competitions Held by FIFA Why is World Cup Finals so important? – Viewer’s Perspective – Player’s Perspective Nationalism

4 World Cup History

5 Most Successful countries

6 Project Understanding Purpose of The Project – Try to estimate match results based on previous world cup finals’ mathces – Try to estimate match scores based on previous world cup finals’ matches’ scores – Try to find meanningfull rules – Figure out which attributes are more important on winning a match.

7 Data Understanding No available data in the Internet! No available data organization for analysis. The very best data I could found

8 Data Understanding The data is far from being enough! New attributes introduced Population, Average Income, Host Information, PastSuccess, CurrentForm, FIFA rank, CurrentClubForm, Match Status Information gathered from FIFA, UEFA, CONMEBOL, CAF, AFC Information is not enough, needs revision

9 Population Larger population may indicate better national team performance Larger means larger talent pool to choose from

10 Average Income How does average income affects national team performance? Football is the poor’s sport or riche’s ?

11 Host Does hosting affect country’s performance ? In fact, it does

12 Past Success Reflects the world cup achievements of a country until specified year. It brings “BIG TEAM” identity. Points Calculation Table

13 Past Success World Cup History Table

14 Current Form Reflects the achivement in last world cup final and the other biggest associated competition’s achivement ( e.g. European Cup, Copa America, Africans Cup, Asian Cup) Points calculated similar to past success, but each competition has different weights

15 Current Form Calculation Table

16 Current Form European Cup Table

17 Club Form Reflects the form of the clubs in a particular country Top 25 clubs according to FIFA found Then for each club, points assigned to corresponding country Based on Champions League, Copa Libertadores, UEFA Cup, League Success

18 Club Form Calculation Table

19 FIFA Rank Reflects the success of each country in last five years along with its club success, league success, international success in all friendly and official qualification and finals matches Lowest rank means most successful country

20 Data Manipulation- Cleaning USA vs United States No longer existing countries – Soviet Union – Yugoslavia Missing Values – Before 1991, FIFA Ranks and Club Forms Missing – Thus, data after 1994 World Cup Final is used

21 Feature Selection # attributes are low No algorithm used Selection done using expert knowledge and some statistical tools

22 Population vs Success

23 GDP vs Success

24 Feature Selection Remove population and GDP 13 attributes left

25 Missing Values Handling Two different tables One with no missing value handling operation – Simply remove rows with missing values The other with using average for missing values

26 Discretization - Normalization Discretization is done for decision tree and bayesian classifiers Normalization is done for SVM, Neural Network and k-NN classifiers

27 Data Visualization Correlation Matrix

28 Data Visualization Box Plot

29 Data Visualization Scatter Plot: Host vs Result

30 Data Visualization Scatter Plot: FIFA Rank1 vs Result

31 Modelling For modelling each classiffier is tested with following different parameters – 5 Fold Cross Validation – 10 Fold Cross Validation – Random Sampling – Stratified Sampling Also discretization and normalization are done before classification

32 KNIME - Modelling

33 Modelling Results of Modelling

34 Modelling Decision Tree gives best result! K-NN gives second best, while others have poor classification Stratified Sampling is generally better than random sampling 10 fold is generally better than 5 fold

35 Decision Tree

36 Tree model indicates the order of attribute importance as follows: 1- Club Form 2- Current Form 3- Past Success 4- Host Information

37 Regression Tree For Regression, regression tree is used in WEKA – The mean absolute error for score1 is 0.43 – The mean absolute error for score2 is 0.38 The errors are high as expected.

38 Association Rules Rules are extracted using WEKA and Knime after discretization of the data – Current Form_2='(-inf-88]' Result=H 439 ==> Club Form_2='(-inf-15.5]' 439 conf:(1) (WEKA)conf:(1) – FIFA Rank_1='(-inf-20.5]' Result=H 374 ==> Club Form_2='(- inf-15.5]' 371 conf:(0.99) (WEKA)conf:(0.99) – Current Form_1='(26-inf)' Result=H 359 ==> Club Form_2='(-inf-15.5]' 354 conf:(0.99) (WEKA)conf:(0.99) – 0.31369426751592355,0.6194968553459119,1.19705853 89453312,"0-10_Past Success_2","<---","[H_Result]“ (KNIME) – 0.40605095541401276,0.8018867924528302,1.12156994 57914863,"0_Club Form_2","<---","[H_Result]“ (KNIME)

39 Conclusion It is hard to collect data manually! The attributes I come up with are better than FIFA rank, so be careful FIFA To sum up, club form is the most important factor for a country to be successful in World Cup Finals After that, current form, past success and host information come

40 Golden Generation


42 Future Work The more number of players from a same club in a particular country, the more likely that country will be successful in World Cup Finals Another data analysis would be on that issue


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